Exploring User Engagement and Incentives in Inquiry-based Active Learning and Knowledge-Sharing Platforms

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Exploring User Engagement and Incentives in Inquiry-based Active 
Learning and Knowledge-Sharing Platforms"

By

Mr. Reza HADI MOGAVI


Abstract

In recent years, active learning and knowledge-sharing platforms 
(henceforth ACKs) have gained recognition as powerful educational tools 
enabling users to learn and practice myriad topics, such as programming 
and foreign language from any place and at any time. However, the high 
rate of user dropouts and low user engagement are impeding users’ 
endeavors to learn, collaborate, and share their knowledge on these 
platforms. Although a vast body of extant studies has examined user 
engagement and incentives in several ACKs such as Connectivist MOOCs, 
significant research gaps remain unaddressed: (1) The research on some 
salient ACKs, including inquiry-based ACKs, remains sparse. The most 
prominent examples of inquiry-based ACKs include Question-based Learning 
platforms (QLs) such as Math Playground and Duolingo, as well as Question 
Answering websites (QAs) such as Stack Overflow and Ask Ubuntu; (2) there 
is a paucity of explicable dropout forecast models for inquiry-based ACKs 
that can determine the underlying reasons for users dropping out of these 
platforms; and (3) a lack of awareness about the reasons behind 
gamification failure in inquiry-based ACKs.

This thesis aims to address these research gaps by adopting a mixed 
quantitative and qualitative research approach. The thesis comprises two 
key segments investigating user engagement and incentives in QL and QA 
platforms, respectively. Each platform entails its unique design 
attributes and subtle nuances that, in turn, require a thorough 
investigation.

When examining QLs, we first characterize the engagement patterns (moods) 
of users over time in a large-scale QL typically used to impart training 
in computational and programming to undergraduate students (users). 
Subsequently, we present a novel hybrid dropout prediction model 
benefitting from the utilization of students’ engagement moods in order to 
enhance the accuracy of dropout predictions in QLs. According to our 
findings, users working on QLs exhibit collective preferences to answer 
questions premised on the engagement mood category with which they are 
associated. Any deviation from these collective preferences significantly 
increases the probability of user dropouts. Gamification denotes a popular 
strategy to avoid or mitigate user dropouts in similar scenarios. 
Nevertheless, in the capacity of an external incentive, it is often 
fraught with its own share of problems. Within this thesis, our subsequent 
study adopts a qualitative research approach to explore one of the most 
pressing concerns in gamification, i.e., an adverse phenomenon alluded to 
as gamification misuse, in a large-scale gamified QL. We undertake careful 
thematic analysis to identify the most common factors underpinning 
gamification misuse, before classifying them into two groups: active and 
passive. To mitigate or prevent the occurrence of gamification misuse in 
their future designs of gamified learning platforms, we also provide 
gamified QLs with a practical set of suggestions.

In the process of studying QAs, we first investigate user dropouts in QAs 
through the lens of flow theory, a well-known psychological theory. The 
theory posits that users tend to be highly engaged in their experience 
when the tasks (assignments) encountered by them are congruent with their 
skill levels. Accordingly, we present a method of new task assignment that 
may help decrease user dropouts in QAs. We then explore promotional 
gamification schemes in QAs in a subsequent study. Promotional 
gamification refers to a temporary gamification scheme added atop an 
already gamified QA to increase user engagement for a short time-span 
(e.g., during the holiday season). This thesis demonstrates that the 
addition of more gamification schemes to a pre-existing gamified platform 
does not always increase user willingness or engagement to contribute more 
to QAs. On the contrary, it risks increased user dropouts and 
overjustification after the removal of additional gamification schemes.

Overall, this thesis provides unique insights to inform researchers’ and 
practitioners’ understanding of user engagement and incentives in 
inquiry-based ACKs, potentially enabling them to reduce users’ dropout 
rates, improve their learning experiences, and obviate unnecessary mishaps 
such as gamification misuse and overjustification.


Date:			Monday, 16 January 2023

Time:			10:00am - 12:00noon

Venue:			Room 5506
 			lifts 25/26

Chairperson:		Prof. Wenbo WANG (MARK)

Committee Members:	Prof. Pan HUI (Supervisor)
 			Prof. Xiaojuan MA (Supervisor)
 			Prof. Ting Chuen PONG
 			Prof. Raymond WONG
 			Prof. Fugee TSUNG (IEDA)
 			Prof. Uichin LEE (KAIST)


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